System for floor number determination in buildings

ABSTRACT

A system may determine a number of floors of a building based on image data of the building. The system may determine a background color of a façade of the building shown in the image data. Using this background color, predefined deviations may then be detected that form the background color in the façade of the building. A mean number of predefined deviations in the vertical direction of the façade of the building may be determined, and the number of floors of the building based on the determined mean number of predefined deviations can be then be deduced.

BACKGROUND OF THE INVENTION

1. Priority Claim

This application claims the benefit of priority from European PatentApplication EP 11 156 653.5 filed on Mar. 2, 2011.

2. Technical Field

The present invention relates to a method for determining a number offloors of a building and to a module system determining the number offloors.

3. Related Art

Navigation systems provide driving recommendations for a driver of avehicle. These navigation systems may provide driving recommendations bysimply indicating an arrow indicating the driving direction.Furthermore, map data showing a network of roads may be displayed in abird's eye perspective to the driver. The recommended route may then behighlighted on the road network shown on the display together withdriving recommendations.

In recent years the displayed navigation information became more andmore user-friendly and it is known to display a three-dimensionalperspective of the scene on a display as seen by the driver. In thiscontext the buildings neighboring the roads are displayed in addition tothe roads.

There exist databases including image data of the road network includingthe vehicle surrounding. In these image data buildings or othermonuments as seen from the road are shown. The amount of data needed toprovide images of the surrounding in a larger geographical area is quitelarge, as normally a picture of a building showing the façade of thebuilding includes approximately 2 Megapixel. These detailed image datamay not be used in connection with a navigation system incorporated intoa vehicle which contain map data of a whole country or a wholecontinent. At the same time a three-dimensional representation of avehicle surrounding is needed in which the user of the map data to whichthe three-dimensional representation is shown may at least recognize thedisplayed buildings shown in vehicle surrounding when the displayedbuildings are compared to the reality when looking out of the window.This is possible when the number of floors of a displayed buildingcorresponds to the number of floors of the building in reality.

Therefore, there exists a need exists to provide a possibility toefficiently and reliably determine the number of floors of a buildingshown on image data.

SUMMARY

According to a first example, a method for determining a number offloors of a building based on image data of the building is provided,the method includes the steps of determining a background color of afaçade of the building shown in the image data. Furthermore, predefineddeviations from the background color are detected in the façade of thebuilding and a mean number of predefined deviations in the verticaldirection of the façade of the building may be determined based on thedetected predefined deviations. Based on the determined mean number ofpredefined deviations the number of floors of a building may be deduced,the number of floors corresponding to the determined mean number. Thebackground color of a façade, the texture, may be determined easily.Windows or doors in the façade have a different color compared to thefaçade. When predefined deviations having the shape of windows or doorsin the background color of the façade are detected, windows or doors inthe façade are detected. From the number of windows/doors in thevertical direction a mean value, the mean number, may be calculatedwhich corresponds to the number of floors with a very high likelihood.When the number of floors of a building may be known together with afloor plan, a schematic view of the building may be generated, whichresembles the look of a building in reality.

According to one example the predefined deviations may be determined bydividing the façade of the building in a plurality of vertical sections.The predefined deviations from the background color may then bedetermined for each vertical section and the mean number of predefineddeviations may be determined based on the respective predefineddeviations determined for the various vertical sections. By determiningthe number of predefined deviations in several sections of the façade,the likelihood that the determined mean number corresponds to the numberof floors is further increased.

One additional possibility to determine the mean number is thedetermination of first vertical sections without predefined deviationswithin the plurality of vertical sections where no predefined deviationsare detected. These first vertical sections without predefineddeviations may then be excluded from the determination of the meandeviation. In the first vertical sections no windows or doors weredetected. Such a vertical section mainly shows the façade withoutwindows. If the number of predefined deviations in this section wereincluded into the determination of the mean deviation, the calculatedmean number would not correspond to the number of floors.

Another possibility to further improve the determination of the meannumber may be the following: within the plurality of vertical sections,second vertical sections are determined in which a number of predefineddeviations differs from the number of predefined deviations determinedin other vertical sections by more than a threshold. For determining thenumber of predefined deviations determined in the other verticalsections, the first vertical sections without deviations may already beexcluded. These second vertical sections may then also be excluded fromthe determination of the mean number. The exclusion of these secondvertical sections helps to further improve the reliability of thedetermination of the number of floors. By way of example a façade maycontain a section in which only one window may be provided in thevertical direction, the building having four or five floors. If thisvertical section were considered for the determination of the meannumber, the result would be distorted. As a consequence, when thesesecond vertical sections are excluded from the determination of the meannumber, the accuracy of the determination of the number of floors may befurther improved.

For determining whether a deviation in the color of the façadecorresponds to a detected window, predefined shapes may be determinedand the detected deviations may be compared to the predefineddeviations. By way of example, a deviation may be considered to be apredefined deviation when a deviation may be substantially rectangularhaving a ratio of width to height that may be within a predefined range.By determining the range, different window formats may be included inthe detection of the windows.

When the number of floors has been determined, it may be possible tostore the number of floors together with other information of thebuilding, allowing a graphical representation of the building to begenerated. When the number of floors of a graphical representationcorresponds to the reality, a user to which the graphical representationmay be shown may easily recognize the displayed building.

One possibility for storing building data allowing a realisticrepresentation of the building with reduced storage capacity may be whenthe number of floors may be stored linked to the floor plan of thebuilding. Based on the floor plan and the number of floors a realisticrepresentation of the building may be generated.

Furthermore, it may be possible when the number of floors may be knownto determine the different floors on the façade of the building based ona distance between vertically neighboring predefined deviations andbased on the number of floors. With this knowledge a display image ofthis building may be generated with different textures for therespective floors of the building. When the displayed information may beused for navigation purposes and when the destination location indicatedby the user may be located in a certain floor, it may be possible toindicate the floor on the display where the location of the destinationmay be situated.

One possibility to determine the different floors may be to determine aboundary between two floors by determining a distance between twovertically neighboring deviations, wherein the boundary may bedetermined to be at half the distance between two vertically neighboringdeviations.

The system furthermore relates to a module for determining the number offloors of the building based on image data of the building, the modulecomprising a processing unit configured to determine a background colorof a façade of the building shown in the image data. The processing unitmay be furthermore configured to detect predefined deviations from thebackground color in the façade of the building and may be configured todetermine a mean number of predefined deviations in the verticaldirection of the façade of the building. Furthermore, the processingunit may be designed to deduce the number of floors of the buildingbased on the determined mean number, wherein the number of floorscorresponds to the determined mean number. The processing unit maydetermine the mean number as discussed in more detail above and below.

Other systems, methods, features and advantages will be, or will become,apparent to one with skill in the art upon examination of the followingfigures and detailed description. It is intended that all suchadditional systems, methods, features and advantages be included withinthis description, be within the scope of the invention, and be protectedby the following claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The system may be better understood with reference to the followingdrawings and description. The components in the figures are notnecessarily to scale, emphasis instead being placed upon illustratingthe principles of the invention. Moreover, in the figures, likereferenced numerals designate corresponding parts throughout thedifferent views.

FIG. 1 illustrates a schematic view of an example of a system allowingthe determination of the number of floors using image data of abuilding.

FIG. 2 illustrates an example flow chart containing the steps carriedout by the module of FIG. 1 to determine the number of floors.

FIG. 3 illustrates a schematic view of an example façade of a buildingand shows how a mean deviation from a background color of the façade maybe determined.

FIG. 4 illustrates a schematic view of an example of a displayedbuilding in which the different floors are shown.

FIG. 5 illustrates a schematic view of an example of how an image of afaçade may be generated knowing the background color and the windows.

FIG. 6 is an example schematic of a system allowing the determination ofthe number of floors using image data of a building

DETAILED DESCRIPTION OF THE PREFERRED EXAMPLES

In FIG. 1, a module 10 is schematically shown with which the number offloors of a building shown in image data may be determined with highaccuracy. The module 10 may be connected to a database 12 comprisingimage data of buildings. These image data may have been generated byvehicles driving on a street network while taking images from thesurrounding. The image data may have further been generated usingairplanes or other flying vehicles. The database containing the imagedata may be directly connected to the module 10. In another example, thedatabase 12 may be located remote from the module 10 and may betransmitted in portions to the module 10. In still other examples, thedatabase 12 may be divided among multiple databases, or other datastorage configurations.

The module 10 may be furthermore connected to map data 13 containing mapinformation as currently used for navigation systems to guide a user toa predefined destination location. For example, the module 10 could be avehicle navigation system. The map data 13 may contain a road networkand may additionally contain information about road surroundingsincluding inter alia buildings. By way of example, the map data 13 maycontain a floor plan of buildings in addition to the road network. Themodule 10 contains a processing unit 11 which may be configured todetermine the number of floors of a building using the image datacontained in the database 12. The determination of the number of floorsof a building will be discussed in further detail with reference toFIGS. 2-5. When the floor number has been determined by the processingunit 11, the floor number of a building may be added to the map datacontained in the database 13. The map data containing the streetnetwork, a floor plan of the buildings and the corresponding number offloors may then be used in connection with a navigation system. Thesystem shown FIG. 1 may be part of a navigation system. However, it maybe also possible that the map data or the floor data determined by themodule 10 are provided to a navigation system for further use.

A method for determining the number of floors is discussed in furtherdetail with reference to FIGS. 2 and 3. In FIG. 3, one example of animage stored in the database 12 is shown in more detail. The image showsa housing 300 with a façade 31. In the example shown, the building hasthree different floors and has a rectangular floor plan.

In connection with FIG. 2, the steps carried out for determining thenumber of floors based on the image data are discussed in more detail.The method starts in step S20 and in the first step the façade may beidentified in the building. As can be seen from FIG. 3, the façade 31 ofa housing unit may be determined in the image by using knownpost-processing algorithms able to detect edges in image data (e.g.Mayny filter or Gaussian filter). By way of example the front façade 31may be delimited against the roof 32 by an eaves-gutter 33, which may benormally a horizontal line. Using edge detection algorithms known in theart, the image may be processed in such a way that the borders of thefaçade such as the left and right border 34 and 35 may be detected.These borders, together with the eaves-gutter form the façade (step 21).In the next step the background color of the façade may be determined bydetermining an RGB value of the façade corresponding to the texture ofthe façade (step S22). Thus, the façade may be detected in images bydetecting a single color surface in a rectangular border. In otherexamples a uniform texture may be detected as the façade needs not tohave a uniform color but may also have a uniform pattern of differentcolors. In urban areas neighboring buildings are often connected to eachother. In order to be able to distinguish two neighboring buildings, thefollowing facts may be taken into account: the height of a building,different colors of the façade, different shape and arrangement ofwindows, and/or different distance of the buildings to the road.

As may be seen in FIG. 3 the façade may be divided into differentvertical sections 36, such as the different sections 36 a to 36 h (stepS23). The different sections are then further processed in order todetect deviations from the background color. The windows 37 or door 38shown in the image will have a different color than the background. Onepossibility to determine colors may be the use of a coded space havingthe form of a cube. The color in this space may be described by threeparameters L, a, b. L describes the lightness of a point, a valueL_(max) representing the white color, a minimum value L_(min) describingthe black color. Parameters a and b do not have boundaries, and apositive a indicates red color, while a negative a represents green. Apositive b value indicates yellow, a negative b indicates blue color. Insection 36 a, the surfaces shown with the dashed lines will be detected.The detected deviations are then categorized and if the detecteddeviation has a shape that corresponds to a predefined shape, thedeviation may be classified as a predefined deviation that may be usedto determine the number of floors of the building. By way of example adeviation found on the façade may be considered to be a predefineddeviation if the deviation has a substantially rectangular shape.Furthermore, a certain range of a ratio of width to height may bedetermined. By way of example every deviation which may be rectangularand has a ratio of width to height between 2:1 to 1:8 may be classifiedas a window or a door. However, it should be understood that a deviationmay also be considered to be a predefined deviation when other shapesare detected, such as round shapes or shapes which are rectangularexcept for the top part, as it may be the case for a door as shown inFIG. 3, where the door 38 is not rectangular at its upper part (stepS24).

The number of deviations may be detected for each vertical section 36a-36 h. As can be seen from FIG. 3 in section 36 c and 36 g no deviationmay be found which falls under the definition of the predefineddeviation.

In the example shown the number of predefined deviations found for eachsection is as follows:

Section 36a 36b 36c 36d 36e 36f 36g 36h Number of 3 3 0 4 1 3 0 1deviations

Three predefined deviations are detected in the vertical direction forsections 36 a, 36 b and 36 f, no predefined deviation in sections 36 cand 36 g, four predefined deviations in section 36 d, and one predefineddeviation in sections 36 e and 36 h. Based on the number of predefineddefinitions found in each section the number of floors may be estimated.To this end the vertical sections where no predefined deviations weredetected are identified (section 36 c and 36 g in the example shown) andthese sections are excluded from the calculation (step S25).Furthermore, two sections are found, here sections 36 e and 36 h, whereonly one deviation was found, whereas three or four deviations werefound in the other remaining sections. After excluding the sectionswithout deviations in step S25, the sections where the number ofdeviations differs greatly from the number of deviations found in othersections are excluded. For this comparison the sections where nodeviations were found are already excluded. Thus, the vertical sectionsin which predefined deviations have been found are compared to eachother, and if the number of deviations found differs from the numberfound in the other sections by more than a predefined threshold (heretwo), the corresponding section is also excluded (step S26). Thus, inthe example shown, sections 36 a and 36 h are excluded from thecalculation. The remaining sections (sections 36 a, 36 b, 36 d and 360are then used for determining a mean number of predefined deviations. Inthe example shown the number of predefined deviations in the remainingsections are as follows: 3, 3, 4, 3. The average deviation for theremaining section is then 3,25 in the example shown. The mean number isnow calculated based on the average by taking the next integer, in theexample shown—3. If the calculated average were 3,8 or 3,9, the number 4would be determined as mean deviation. Thus, the next integer locatedcloser to the determined average may be used as mean number (step S27).In the example shown the calculated number of predefined deviations(windows or doors) is 3. In the next step the number of floors may bededuced from the determined mean number, the number of floors simplycorresponding to the mean number, in the example shown 3 (step S28).When the calculation determined in step S28 is compared to the Figureshown in FIG. 3, it may be deduced that the correct number of floors wasdeduced from the image processing. In step S29 the number of floors maybe stored together with other information in the map database 13, e.g.together with the floor plan. The method ends in step S30. In theexample shown, the mean deviation was determined by rounding thedetermined average to the next integer.

In 4, an example is shown where a building 40 with a known floor plan 41is shown. It may be assumed that the number of floors has beendetermined as explained above in connection with FIGS. 2 and 3. In theexample shown the determined number of floors is 4. The processing unitshown in FIG. 1 may furthermore be configured to generate image datausing the ground floor information and the information about the numberof floors. With these two pieces of information a realisticrepresentation how a building looks like is obtained.

Furthermore, the processing unit 11 may be configured to determine thedifferent floors in the building using the knowledge of the determinednumber of floors and using the detected predefined deviations. In theexample shown in FIG. 4 two windows 42 and 43 are shown. These twowindows were detected as predefined deviation as discussed above. Whentwo neighboring windows in the vertical direction were identified, adistance between two neighboring windows may be determined on the imagedata. With this knowledge the boundary between two floors may bedetermined by taking half of the distance between the two windows 42 and43. For this determination of the boundary between two floors verticalsections may be used in which the number of predefined deviationscorresponds to the determined number of floors or mean deviation. Thishelps to avoid that different boundaries or a wrong boundary aredetermined for one building. Knowing the boundary between two floors, itmay be possible to display the different floors with different textures.This makes it easy for the user to differentiate the different floors.If the displayed image of the building may be used in connection with anavigation system and if the floor number of a desired destination maybe known, it may be possible to indicate in detail the floor in whichthe desired destination may be located.

The information about the determined number of floors and theinformation of the background color may also be used to store the imagedata in the following way: knowing the background color, the color ofthe façade, a first layer with the dimensions of the façade may begenerated having the color of the determined background. In the exampleshown in FIG. 5 this layer, the background texture is shown withreference numeral 51. Furthermore, the windows or doors had beendetermined using the detection of the predefined deviations. A seconddata layer may be generated including the detected windows. In the upperright part of FIG. 5 the window layer is shown by reference numeral 52.These two layers may then be combined to generate a coloredrepresentation as shown in the image 53 in the lower part of FIG. 5.This representation may then be displayed to the user.

When the predefined deviations are detected in the façade, it may bepossible that different shapes of windows and doors are detected. Thedifferent shapes may then be stored in a library and this library may beused to identify the predefined deviations by comparing the deviationsfound on a façade to the known predefined deviations stored in thelibrary. If the detected shape falls within the predefined deviationsbut is not yet included in the library of shapes, the newly found formof the deviation may be added to the library and classified as being apredefined deviation.

FIG. 6 is an example of a system for floor number determination inbuildings 60. In addition to one or more inputs 62 and one or moreoutputs 64, the system 60 may include a processor 66, a memory 68,software 70, and an interface 72. The system 60 may include analogsignal processing and digital signal processing capability.

The processor 66 may include one or more devices capable of executinginstructions to perform one or more operations within the system 60. InFIG. 7, the processor 66 is incorporated into the system 60. Theprocessor 66 may be one or more general processors, digital signalprocessors (“DSP”), application specific integrated circuits (“ASIC”),field programmable gate arrays (“FPGA”), server computers, networks,digital circuits, analog circuits, combinations thereof, or other nowknown or later developed devices for analyzing and processing digitaland analogue data. The processor 66 may operate in conjunction with asoftware program, such as instructions or code and data stored in thesystem 60.

The processor 66 may be coupled with memory 68, or memory 68 may be aseparate component. Software 70 may be stored in memory 68. Memory 68may include, but is not limited to, computer readable storage media suchas various types of volatile and non-volatile storage media, includingrandom access memory, read-only memory, programmable read-only memory,electrically programmable read-only memory, electrically erasableread-only memory, flash memory, magnetic tape or disk, optical media andthe like. The memory 68 may include a random access memory for theprocessor. Alternatively, the memory 68 may be separate from theprocessor, such as a cache memory of a processor, the system memory, orother memory. The memory may be an external storage device or databasefor storing recorded data. Examples include a hard drive, compact disc(“CD”), digital video disc (“DVD”), memory card, memory stick, floppydisc, universal serial bus (“USB”) memory device, or any other deviceoperative to store data. The memory 68 may be operable to storeinstructions executable by the processor.

The system 60 may have an interface 72. The interface 72 may includeknobs, switches, sliding components, buttons, a mouse, keyboard, adisplay, a touch screen or other devices or mechanisms capable ofreceiving user inputs for adjusting, modifying or controlling one ormore features of the system 60 and providing outputs sensed by a user.The interface 72 may be used to manipulate one or more characteristics,components, or features of the system 60. For example, the system 60 mayinclude a slider which, when moved, modifies the volume for one or moreof the received signals processed by the console. In another example,the interface 72 may include a knob, that when turned, modifies the gainapplied by one or more amplifiers in the system 60. In still anotherexample, the interface 60 may be a data input location displayed in adisplay and a corresponding data input device in which parameters, suchas a gain, a threshold, or any other parameter may be entered by a userof the system 60.

The functions, acts, tasks, and/or components described herein may beperformed or represented by a programmed processor executinginstructions stored in memory. The functions, acts or tasks may beindependent of the particular type of instruction set, storage media,processor or processing strategy and may be performed by software,hardware, integrated circuits, firm-ware, micro-code and the like,operating alone or in combination. Likewise, processing strategies mayinclude multiprocessing, multitasking, parallel processing and the like.A processor may be configured to execute the software.

While various examples of the invention have been described, it will beapparent to those of ordinary skill in the art that many more examplesand implementations are possible within the scope of the invention.Accordingly, the invention is not to be restricted except in light ofthe attached claims and their equivalents.

I claim:
 1. A method for determining a number of floors of a buildingbased on image data of the building, the method comprising the steps of:determining by a processing unit a background color of a façade of thebuilding shown in the image data; detecting by the processing unitpredefined deviations from the background color in the façade of thebuilding; determining by the processing unit a mean number of predefineddeviations in a vertical direction of the façade of the building basedon the detected predefined deviations; and deducing by the processingunit the number of floors of the building based on the determined meannumber of predefined deviations, wherein the number of floorscorresponds to the determined mean number; wherein the predefineddeviations are determined by dividing the façade of the building in aplurality of vertical sections, wherein the predefined deviations fromthe background color are determined for each vertical section, wherein amean number of predefined deviations may be determined based on thepredefined deviations determined for the vertical sections.
 2. Themethod according to claim 1, wherein the step of determining the meannumber of predefined deviations further comprises: determining by theprocessing unit second vertical sections within the plurality ofvertical sections, in which a number of predefined deviations differsfrom the number of predefined deviations determined in third verticalsections by more than a threshold; and excluding by the processing unitsaid second vertical sections from the determination of the mean numberof predefined deviations.
 3. The method according to claim 1, whereinthe step of determining the mean number of predefined deviations furthercomprises: determining by the processing unit first vertical sectionswithout predefined deviations within the plurality of vertical sectionswhere no predefined deviations are detected; and excluding by theprocessing unit said first vertical sections without predefineddeviations from the determination of the mean number of predefineddeviations.
 4. The method according to claim 3, wherein the step ofdetermining the mean number of predefined deviations further comprises:determining by the processing unit second vertical sections within theplurality of vertical sections, in which a third number of predefineddeviations differs from the number of predefined deviations determinedin other vertical sections by more than a threshold; and excluding bythe processing unit said second vertical sections from the determinationof the mean number of predefined deviations.
 5. The method accordingclaim 1, wherein a deviation in the façade is a predefined deviationwhen the deviation is substantially rectangular having a ratio of widthto height that is within a predefined range.
 6. The method accordingclaim 1, wherein the number of floors is linked to other information ofthe building allowing a graphical representation of the building to begenerated.
 7. The method according to claim 6, wherein the number offloors is linked to a floor plan of the building.
 8. The methodaccording to claim 1, further comprising the step of generating by theprocessing unit a display image of the building using a floor plan ofthe building and the determined number of floors.
 9. The methodaccording claim 1, further comprising the steps of: determining by theprocessing unit different floors on the façade of the building based ona vertical distance between vertically neighbouring predefineddeviations and based on the deduced number of floors; and generating bythe processing unit a display image of the building with differenttextures for the respective different floors of the building.
 10. Themethod according to claim 9, wherein a boundary between two floors isdetermined by determining a distance between two vertically neighbouringpredefined deviations, wherein the boundary is determined to be at halfthe distance between two vertically neighbouring deviations.
 11. Asystem for determining a number of floors of a building based on imagedata of the building, the system comprising a processing unit, theprocessing unit configured to: determine a background color of a façadeof the building shown in the image data; detect predefined deviationsfrom the background color in the façade of the building; determine amean number of predefined deviations in a vertical direction of thefaçade of the building; deduce the number of floors of the buildingbased on the determined mean number of predefined deviations, whereinthe number of floors corresponds to the determined mean number; whereinthe processing unit is further configured to determine the predefineddeviations by dividing the façade of the building in a plurality ofvertical sections, wherein the predefined deviations from the backgroundcolor are determined for each vertical section, wherein the mean numberof predefined deviations is determined based on the predefineddeviations determined for the vertical sections.
 12. The systemaccording to claim 11, wherein the processing unit is further configuredto: determine second vertical sections within the plurality of verticalsections, in which a number of predefined deviations differs from thenumber of predefined deviations determined in third vertical sections bymore than a threshold; and exclude said second vertical sections fromthe determination of the mean number of predefined deviations.
 13. Thesystem according to claim 11, wherein the processing unit is furtherconfigured to: determine first vertical sections without predefineddeviations within the plurality of vertical sections where no predefineddeviations are detected; and exclude said first vertical sectionswithout predefined deviations from the determination of the mean numberof predefined deviations.
 14. The system according to claim 13, whereinthe processing unit is further configured to: determine second verticalsections within the plurality of vertical sections, in which a number ofpredefined deviations differs from the number of predefined deviationsdetermined in third vertical sections by more than a threshold; andexclude said second vertical sections from the determination of the meannumber of predefined deviations.
 15. The system according claim 11,wherein a deviation in the façade is a predefined deviation when thedeviation is substantially rectangular having a ratio of width to heightthat is within a predefined range.
 16. The system according claim 11,wherein the number of floors is linked to other information of thebuilding allowing a graphical representation of the building to begenerated.
 17. The method according to claim 16, wherein the number offloors is linked to a floor plan of the building.
 18. The systemaccording to claim 11, wherein the processing unit is further configuredto generate a display image of the building using a floor plan of thebuilding and the determined number of floors.
 19. The system accordingclaim 11, wherein the processing unit is further configured to:determine different floors on the façade of the building based on avertical distance between vertically neighbouring predefined deviationsand based on the deduced number of floors; and generate a display imageof the building with different textures for the respective differentfloors of the building.
 20. The system according to claim 19, wherein aboundary between two floors is determined by determination of a distancebetween two vertically neighbouring predefined deviations by theprocessing unit, where the boundary is determined to be at half thedistance between two vertically neighbouring deviations.